Triple

T22121211
Position Surface form Disambiguated ID Type / Status
Subject תּוֹלָע E546671 entity
Predicate succeededBy P78 FINISHED
Object Jair NE NERFINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Jair | Statement: [תּוֹלָע, succeededBy, Jair]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jair
Context triple: [תּוֹלָע, succeededBy, Jair]
  • A. Jair chosen
    Jair is a minor biblical judge of Israel mentioned in the Book of Judges, known for his leadership and his thirty sons who rode thirty donkeys and controlled thirty towns.
  • B. Pedro Malan
    Pedro Malan is a Brazilian economist and former finance minister known for his central role in stabilizing Brazil’s economy during the 1990s.
  • C. Jader
    Jader was an important ancient coastal city in the Roman province of Dalmatia, located on the eastern shore of the Adriatic Sea in what is now Zadar, Croatia.
  • D. Marcos
    Marcos is a municipality in the province of Ilocos Norte in the Philippines, known for its agricultural economy and rural communities.
  • E. Marcos
    Marcos is a masculine given name, commonly used in Spanish- and Portuguese-speaking countries, that derives from the Latin name Marcus.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69e11e38b3848190ac3a4fa97d56e65a completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1297e7e188190873924403421caa2 completed April 28, 2026, 9:41 p.m.
Created at: April 16, 2026, 8:31 p.m.